Together AI· Inference platform

How to evaluate and benchmark Large Language Models (LLMs)

TL;DR Learn how to evaluate and benchmark large language models using datasets like MMLU, GSM8K, and HumanEval. Going further, we’ll also explore methods and best practices for reliable, real-world LLM performance testing. Large language models (LLMs) have transformed how we interact with AI, from powering chatbots to generating code and solving complex mathematical problems. But as these models become increasingly sophisticated, a critical question remains: How do we actually measure their capabilities and determine which models are truly better ? The answer is benchmarks and evaluation frameworks — the systematic approaches we use to test, compare, and understand LLM performance. Understanding how to properly evaluate LLMs is essential. In this blog we'll explore everything you need to know about LLM evaluation, from the fundamental principles of good benchmarks to the various evaluation methodologies used in practice. Additionally, we also include practical code notebooks to help you get hands on running evals for real-world use-cases we see our customers working on everyday! Why LLM benchmarks and evaluations matter The foundation of progress …

Announced

04 Nov 2025 00:00 UTC

Detected

03 Aug 2026 21:12 UTC (+272d after)